MBO_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab41aebd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir(\"./PMBO\") #you own PMBO download path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c805c218",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import torch\n",
    "import gpytorch\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c5c3f89b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a814fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utility import centering,paretoOpt,Initialdata_Preparation\n",
    "from hypervolume import InnerHyperVolume\n",
    "from log import log\n",
    "from optimize import MBO_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f578e07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SMILES</th>\n",
       "      <th>RI</th>\n",
       "      <th>Tg</th>\n",
       "      <th>TC</th>\n",
       "      <th>MW_ratio</th>\n",
       "      <th>Mass_ave</th>\n",
       "      <th>K_bond_min</th>\n",
       "      <th>K_bond_ave</th>\n",
       "      <th>K_ang_ave</th>\n",
       "      <th>...</th>\n",
       "      <th>AATSC2Z</th>\n",
       "      <th>BCUTd-1h</th>\n",
       "      <th>BCUTZ-1h</th>\n",
       "      <th>nHBDon</th>\n",
       "      <th>MIC0</th>\n",
       "      <th>Kier3</th>\n",
       "      <th>Mor02</th>\n",
       "      <th>Mor19</th>\n",
       "      <th>SMR_VSA1</th>\n",
       "      <th>SMR_VSA3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PI1</td>\n",
       "      <td>*CC*</td>\n",
       "      <td>1.512</td>\n",
       "      <td>-41.444976</td>\n",
       "      <td>0.456185</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.675667</td>\n",
       "      <td>218.51</td>\n",
       "      <td>325.816667</td>\n",
       "      <td>48.657000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.833333</td>\n",
       "      <td>2.111099</td>\n",
       "      <td>6.102019</td>\n",
       "      <td>0</td>\n",
       "      <td>6.509500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13.561131</td>\n",
       "      <td>0.205252</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PI2</td>\n",
       "      <td>*C(C*)C</td>\n",
       "      <td>1.491</td>\n",
       "      <td>-12.961252</td>\n",
       "      <td>0.258773</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>4.675667</td>\n",
       "      <td>218.51</td>\n",
       "      <td>326.680000</td>\n",
       "      <td>49.103167</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.593205</td>\n",
       "      <td>3.021853</td>\n",
       "      <td>6.150638</td>\n",
       "      <td>0</td>\n",
       "      <td>6.621049</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>19.229187</td>\n",
       "      <td>0.421298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PI3</td>\n",
       "      <td>*C(C*)CC</td>\n",
       "      <td>1.475</td>\n",
       "      <td>-17.648048</td>\n",
       "      <td>0.264363</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>4.675667</td>\n",
       "      <td>218.51</td>\n",
       "      <td>327.040000</td>\n",
       "      <td>48.953250</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.388605</td>\n",
       "      <td>3.025654</td>\n",
       "      <td>6.168683</td>\n",
       "      <td>0</td>\n",
       "      <td>6.667362</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>21.929279</td>\n",
       "      <td>0.350261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PI4</td>\n",
       "      <td>*C(C*)CCC</td>\n",
       "      <td>1.467</td>\n",
       "      <td>-28.994172</td>\n",
       "      <td>0.261131</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.675667</td>\n",
       "      <td>218.51</td>\n",
       "      <td>327.256000</td>\n",
       "      <td>48.866333</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.310727</td>\n",
       "      <td>3.025703</td>\n",
       "      <td>6.178959</td>\n",
       "      <td>0</td>\n",
       "      <td>6.690928</td>\n",
       "      <td>3.840000</td>\n",
       "      <td>24.753693</td>\n",
       "      <td>0.823443</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PI5</td>\n",
       "      <td>*C(C*)C(C)C</td>\n",
       "      <td>1.456</td>\n",
       "      <td>31.319970</td>\n",
       "      <td>0.234342</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.675667</td>\n",
       "      <td>218.51</td>\n",
       "      <td>327.256000</td>\n",
       "      <td>49.203833</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.477393</td>\n",
       "      <td>3.113397</td>\n",
       "      <td>6.196681</td>\n",
       "      <td>0</td>\n",
       "      <td>6.690928</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>25.662506</td>\n",
       "      <td>0.819376</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1070</th>\n",
       "      <td>PI1213</td>\n",
       "      <td>[*]C=Cc1ccc(C=Cc2nc3cc4nc([*])[nH]c4cc3[nH]2)cc1</td>\n",
       "      <td>1.849</td>\n",
       "      <td>291.213887</td>\n",
       "      <td>0.893000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.362412</td>\n",
       "      <td>349.52</td>\n",
       "      <td>392.020526</td>\n",
       "      <td>65.666710</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009160</td>\n",
       "      <td>3.214789</td>\n",
       "      <td>7.073043</td>\n",
       "      <td>2</td>\n",
       "      <td>11.471507</td>\n",
       "      <td>4.065600</td>\n",
       "      <td>35.885536</td>\n",
       "      <td>-0.125057</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.935914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1071</th>\n",
       "      <td>PI1214</td>\n",
       "      <td>[*]Nc1ccc(-c2ccc(NC3=CC(=O)C([*])=CC3=O)cc2)cc1</td>\n",
       "      <td>1.735</td>\n",
       "      <td>228.347696</td>\n",
       "      <td>0.379000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.479588</td>\n",
       "      <td>277.60</td>\n",
       "      <td>393.051351</td>\n",
       "      <td>63.475111</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>3.158248</td>\n",
       "      <td>8.023216</td>\n",
       "      <td>2</td>\n",
       "      <td>13.489312</td>\n",
       "      <td>4.803403</td>\n",
       "      <td>30.533248</td>\n",
       "      <td>0.116230</td>\n",
       "      <td>9.589074</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1072</th>\n",
       "      <td>PI1215</td>\n",
       "      <td>[*]NC1=CC(=O)C([*])=CC1=O</td>\n",
       "      <td>1.657</td>\n",
       "      <td>164.128637</td>\n",
       "      <td>0.347000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.091250</td>\n",
       "      <td>295.35</td>\n",
       "      <td>412.541539</td>\n",
       "      <td>68.149649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605442</td>\n",
       "      <td>3.156103</td>\n",
       "      <td>8.023195</td>\n",
       "      <td>1</td>\n",
       "      <td>16.998058</td>\n",
       "      <td>1.975309</td>\n",
       "      <td>13.480738</td>\n",
       "      <td>0.013218</td>\n",
       "      <td>9.589074</td>\n",
       "      <td>5.733667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1073</th>\n",
       "      <td>PI1216</td>\n",
       "      <td>[*]Nc1ccc([*])nc1</td>\n",
       "      <td>1.683</td>\n",
       "      <td>150.099978</td>\n",
       "      <td>0.398000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.372818</td>\n",
       "      <td>349.52</td>\n",
       "      <td>396.544167</td>\n",
       "      <td>67.789316</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.719091</td>\n",
       "      <td>3.059415</td>\n",
       "      <td>7.044647</td>\n",
       "      <td>1</td>\n",
       "      <td>12.714831</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.627242</td>\n",
       "      <td>-0.196078</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.983979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1074</th>\n",
       "      <td>PI1217</td>\n",
       "      <td>[*]Nc1nnc(Nc2n[nH]c([*])n2)[nH]1</td>\n",
       "      <td>1.732</td>\n",
       "      <td>201.566278</td>\n",
       "      <td>0.451000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.258250</td>\n",
       "      <td>290.67</td>\n",
       "      <td>410.282778</td>\n",
       "      <td>86.276607</td>\n",
       "      <td>...</td>\n",
       "      <td>0.837302</td>\n",
       "      <td>3.084587</td>\n",
       "      <td>7.181570</td>\n",
       "      <td>4</td>\n",
       "      <td>13.561007</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>19.430710</td>\n",
       "      <td>0.077295</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.362684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1075 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID                                            SMILES     RI  \\\n",
       "0        PI1                                              *CC*  1.512   \n",
       "1        PI2                                           *C(C*)C  1.491   \n",
       "2        PI3                                          *C(C*)CC  1.475   \n",
       "3        PI4                                         *C(C*)CCC  1.467   \n",
       "4        PI5                                       *C(C*)C(C)C  1.456   \n",
       "...      ...                                               ...    ...   \n",
       "1070  PI1213  [*]C=Cc1ccc(C=Cc2nc3cc4nc([*])[nH]c4cc3[nH]2)cc1  1.849   \n",
       "1071  PI1214   [*]Nc1ccc(-c2ccc(NC3=CC(=O)C([*])=CC3=O)cc2)cc1  1.735   \n",
       "1072  PI1215                         [*]NC1=CC(=O)C([*])=CC1=O  1.657   \n",
       "1073  PI1216                                 [*]Nc1ccc([*])nc1  1.683   \n",
       "1074  PI1217                  [*]Nc1nnc(Nc2n[nH]c([*])n2)[nH]1  1.732   \n",
       "\n",
       "              Tg        TC  MW_ratio   Mass_ave  K_bond_min  K_bond_ave  \\\n",
       "0     -41.444976  0.456185  1.000000   4.675667      218.51  325.816667   \n",
       "1     -12.961252  0.258773  0.666667   4.675667      218.51  326.680000   \n",
       "2     -17.648048  0.264363  0.500000   4.675667      218.51  327.040000   \n",
       "3     -28.994172  0.261131  0.400000   4.675667      218.51  327.256000   \n",
       "4      31.319970  0.234342  0.400000   4.675667      218.51  327.256000   \n",
       "...          ...       ...       ...        ...         ...         ...   \n",
       "1070  291.213887  0.893000  1.000000   8.362412      349.52  392.020526   \n",
       "1071  228.347696  0.379000  1.000000   8.479588      277.60  393.051351   \n",
       "1072  164.128637  0.347000  1.000000  10.091250      295.35  412.541539   \n",
       "1073  150.099978  0.398000  1.000000   8.372818      349.52  396.544167   \n",
       "1074  201.566278  0.451000  1.000000  10.258250      290.67  410.282778   \n",
       "\n",
       "      K_ang_ave  ...   AATSC2Z  BCUTd-1h  BCUTZ-1h  nHBDon       MIC0  \\\n",
       "0     48.657000  ... -1.833333  2.111099  6.102019       0   6.509500   \n",
       "1     49.103167  ... -1.593205  3.021853  6.150638       0   6.621049   \n",
       "2     48.953250  ... -1.388605  3.025654  6.168683       0   6.667362   \n",
       "3     48.866333  ... -1.310727  3.025703  6.178959       0   6.690928   \n",
       "4     49.203833  ... -0.477393  3.113397  6.196681       0   6.690928   \n",
       "...         ...  ...       ...       ...       ...     ...        ...   \n",
       "1070  65.666710  ... -0.009160  3.214789  7.073043       2  11.471507   \n",
       "1071  63.475111  ... -0.111111  3.158248  8.023216       2  13.489312   \n",
       "1072  68.149649  ...  0.605442  3.156103  8.023195       1  16.998058   \n",
       "1073  67.789316  ... -0.719091  3.059415  7.044647       1  12.714831   \n",
       "1074  86.276607  ...  0.837302  3.084587  7.181570       4  13.561007   \n",
       "\n",
       "         Kier3      Mor02     Mor19  SMR_VSA1   SMR_VSA3  \n",
       "0     4.000000  13.561131  0.205252  0.000000   0.000000  \n",
       "1     4.000000  19.229187  0.421298  0.000000   0.000000  \n",
       "2     3.000000  21.929279  0.350261  0.000000   0.000000  \n",
       "3     3.840000  24.753693  0.823443  0.000000   0.000000  \n",
       "4     2.666667  25.662506  0.819376  0.000000   0.000000  \n",
       "...        ...        ...       ...       ...        ...  \n",
       "1070  4.065600  35.885536 -0.125057  0.000000  19.935914  \n",
       "1071  4.803403  30.533248  0.116230  9.589074   0.000000  \n",
       "1072  1.975309  13.480738  0.013218  9.589074   5.733667  \n",
       "1073  2.000000  14.627242 -0.196078  0.000000   4.983979  \n",
       "1074  2.750000  19.430710  0.077295  0.000000  30.362684  \n",
       "\n",
       "[1075 rows x 51 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv(r'../example.csv',index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d4dd5cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "RI_feature=['MW_ratio', 'Mass_ave', 'K_bond_min', 'K_bond_ave',\n",
    "           'K_ang_ave', 'Theta0_ave', 'K_dih_max', 'K_dih_ave', 'VE1_A', 'AATS0d',\n",
    "           'AATS0Z', 'BalabanJ', 'HybRatio', 'SaasC', 'ETA_dBeta', 'IC1', 'IC3',\n",
    "           'MIC1', 'Mor14', 'SMR_VSA5', 'VSA_EState4', 'VSA_EState8']\n",
    "Tg_feature=['K_bond_ave', 'Theta0_ave', 'VR3_A', 'nN', 'AATS0d',\n",
    "           'AATS2d', 'ATSC1d', 'ATSC3d', 'BalabanJ', 'Xch-6d', 'IC3',\n",
    "           'SlogP_VSA1']\n",
    "TC_feature=['Monomer_length', 'MW_ratio', 'Mass_max',\n",
    "           'Mass_ave', 'K_bond_ave', 'K_ang_ave', 'nH', 'AATS0d', 'AATS0Z',\n",
    "           'ATSC2Z', 'ATSC5Z', 'AATSC0Z', 'AATSC2Z', 'BCUTd-1h', 'BCUTZ-1h',\n",
    "           'nHBDon', 'MIC0', 'MIC1', 'Kier3', 'Mor02', 'Mor14', 'Mor19',\n",
    "           'SMR_VSA1', 'SMR_VSA3', 'VSA_EState4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07273473",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_RI=df[RI_feature]\n",
    "X_Tg=df[Tg_feature]\n",
    "X_TC=df[TC_feature]\n",
    "Y_RI=df[\"RI\"].values\n",
    "Y_Tg=df[\"Tg\"].values\n",
    "Y_TC=df[\"TC\"].values\n",
    "Xinfo = df['ID']\n",
    "Xdata= np.hstack((X_RI.values,X_Tg.values,X_TC.values))\n",
    "X_layer=np.vstack((0,X_RI.shape[1],X_Tg.shape[1],X_TC.shape[1])).T\n",
    "Ydata = np.vstack((Y_RI,Y_Tg,Y_TC)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "512b393d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_all=centering(Xdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "32d73435",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_layer=np.sum(X_layer)\n",
    "nobj=Ydata.shape[1]\n",
    "random_data=20\n",
    "seed=2\n",
    "Multiprobe=5\n",
    "Nruns=16\n",
    "metr = 'crowdingDistance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b53f95d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_point=[1.85,405.65,1.08]\n",
    "HV = InnerHyperVolume(ref_point)\n",
    "Paretoloc, ybestloc = paretoOpt(Ydata,metric=metr,opt='max') \n",
    "volumemax=HV.compute(Ydata[Paretoloc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd5e575e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
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      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m***\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m***\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m***\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m***\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\n",
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      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*****\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m******\n",
      "****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**************\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m****\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m**********\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m\u001b[1m#\u001b[0m*******\n",
      "\n",
      "\n",
      "Randomly selected 20 data for initial training \n",
      "The best from random datasets\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.764898 \n",
      "\n",
      "Multi-objective optimization #RUN 1\n",
      "Iter 1000/5000 - Loss: -1.732  res: 0.00001492\n",
      "Iter 2000/5000 - Loss: -1.746  res: 0.00000450\n",
      "Iter 3000/5000 - Loss: -1.752  res: 0.00000223\n",
      "Iter 4000/5000 - Loss: -1.755  res: 0.00000135\n",
      "Iter 5000/5000 - Loss: -1.757  res: 0.00003161\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 280.752  res: 0.00065949\n",
      "Iter 2000/5000 - Loss: 170.335  res: 0.00039727\n",
      "Iter 3000/5000 - Loss: 121.234  res: 0.00029183\n",
      "Iter 4000/5000 - Loss: 93.825  res: 0.00022907\n",
      "Iter 5000/5000 - Loss: 76.087  res: 0.00019331\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -1.343  res: 0.00002582\n",
      "Iter 2000/5000 - Loss: -1.364  res: 0.00000897\n",
      "Iter 3000/5000 - Loss: -1.373  res: 0.00000472\n",
      "Iter 4000/5000 - Loss: -1.378  res: 0.00000505\n",
      "Iter 5000/5000 - Loss: -1.381  res: 0.00000202\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1210   RI: 1.817000   Tg: 382.291980   TC:0.391000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.764898 \n",
      "\n",
      "Multi-objective optimization #RUN 2\n",
      "Iter 1000/5000 - Loss: -1.706  res: 0.00001318\n",
      "Iter 2000/5000 - Loss: -1.718  res: 0.00000421\n",
      "Iter 3000/5000 - Loss: -1.723  res: 0.00005826\n",
      "Iter 4000/5000 - Loss: -1.726  res: 0.00005433\n",
      "Iter 5000/5000 - Loss: -1.728  res: 0.00000085\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 263.173  res: 0.00065610\n",
      "Iter 2000/5000 - Loss: 160.217  res: 0.00039497\n",
      "Iter 3000/5000 - Loss: 114.007  res: 0.00029505\n",
      "Iter 4000/5000 - Loss: 87.806  res: 0.00023166\n",
      "Iter 5000/5000 - Loss: 71.033  res: 0.00019136\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.710  res: 0.00002049\n",
      "Iter 2000/5000 - Loss: -0.718  res: 0.00000597\n",
      "Iter 3000/5000 - Loss: -0.720  res: 0.00035058\n",
      "Iter 4000/5000 - Loss: -0.722  res: 0.00000174\n",
      "Iter 5000/5000 - Loss: -0.723  res: 0.00000116\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1207   RI: 1.781000   Tg: 396.400664   TC:0.361000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.958440 \n",
      "\n",
      "Multi-objective optimization #RUN 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 1000/5000 - Loss: -1.718  res: 0.00000963\n",
      "Iter 2000/5000 - Loss: -1.727  res: 0.00000166\n",
      "Iter 3000/5000 - Loss: -1.730  res: 0.00018262\n",
      "Iter 4000/5000 - Loss: -1.733  res: 0.00006506\n",
      "Iter 5000/5000 - Loss: -1.734  res: 0.00000067\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 250.455  res: 0.00064461\n",
      "Iter 2000/5000 - Loss: 153.679  res: 0.00039046\n",
      "Iter 3000/5000 - Loss: 109.340  res: 0.00030019\n",
      "Iter 4000/5000 - Loss: 83.524  res: 0.00024094\n",
      "Iter 5000/5000 - Loss: 67.094  res: 0.00020083\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.670  res: 0.00001893\n",
      "Iter 2000/5000 - Loss: -0.677  res: 0.00000532\n",
      "Iter 3000/5000 - Loss: -0.679  res: 0.00000340\n",
      "Iter 4000/5000 - Loss: -0.681  res: 0.00008598\n",
      "Iter 5000/5000 - Loss: -0.682  res: 0.00000101\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1207   RI: 1.781000   Tg: 396.400664   TC:0.361000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.966275 \n",
      "\n",
      "Multi-objective optimization #RUN 4\n",
      "Iter 1000/5000 - Loss: -1.836  res: 0.00001696\n",
      "Iter 2000/5000 - Loss: -1.853  res: 0.00000489\n",
      "Iter 3000/5000 - Loss: -1.859  res: 0.00000242\n",
      "Iter 4000/5000 - Loss: -1.863  res: 0.00000373\n",
      "Iter 5000/5000 - Loss: -1.865  res: 0.00000041\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 233.888  res: 0.00064430\n",
      "Iter 2000/5000 - Loss: 143.771  res: 0.00038869\n",
      "Iter 3000/5000 - Loss: 102.205  res: 0.00030212\n",
      "Iter 4000/5000 - Loss: 77.866  res: 0.00024488\n",
      "Iter 5000/5000 - Loss: 62.242  res: 0.00020685\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.698  res: 0.00001767\n",
      "Iter 2000/5000 - Loss: -0.704  res: 0.00041394\n",
      "Iter 3000/5000 - Loss: -0.706  res: 0.00000225\n",
      "Iter 4000/5000 - Loss: -0.708  res: 0.00000101\n",
      "Iter 5000/5000 - Loss: -0.708  res: 0.00000089\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.966275 \n",
      "\n",
      "Multi-objective optimization #RUN 5\n",
      "Iter 1000/5000 - Loss: -1.795  res: 0.00001192\n",
      "Iter 2000/5000 - Loss: -1.808  res: 0.00000388\n",
      "Iter 3000/5000 - Loss: -1.813  res: 0.00000200\n",
      "Iter 4000/5000 - Loss: -1.815  res: 0.00000121\n",
      "Iter 5000/5000 - Loss: -1.817  res: 0.00000867\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 218.588  res: 0.00064582\n",
      "Iter 2000/5000 - Loss: 134.486  res: 0.00038681\n",
      "Iter 3000/5000 - Loss: 95.912  res: 0.00029918\n",
      "Iter 4000/5000 - Loss: 73.143  res: 0.00024590\n",
      "Iter 5000/5000 - Loss: 58.324  res: 0.00020944\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.647  res: 0.00001281\n",
      "Iter 2000/5000 - Loss: -0.652  res: 0.00000368\n",
      "Iter 3000/5000 - Loss: -0.653  res: 0.00000177\n",
      "Iter 4000/5000 - Loss: -0.654  res: 0.00000105\n",
      "Iter 5000/5000 - Loss: -0.655  res: 0.00000070\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.972094 \n",
      "\n",
      "Multi-objective optimization #RUN 6\n",
      "Iter 1000/5000 - Loss: -1.845  res: 0.00001052\n",
      "Iter 2000/5000 - Loss: -1.856  res: 0.00002035\n",
      "Iter 3000/5000 - Loss: -1.861  res: 0.00000195\n",
      "Iter 4000/5000 - Loss: -1.864  res: 0.00009743\n",
      "Iter 5000/5000 - Loss: -1.866  res: 0.00000081\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 204.305  res: 0.00064014\n",
      "Iter 2000/5000 - Loss: 126.280  res: 0.00038302\n",
      "Iter 3000/5000 - Loss: 90.337  res: 0.00029694\n",
      "Iter 4000/5000 - Loss: 69.014  res: 0.00024426\n",
      "Iter 5000/5000 - Loss: 55.182  res: 0.00020522\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.457  res: 0.00000006\n",
      "Iter 2000/5000 - Loss: -0.459  res: 0.00000727\n",
      "Iter 3000/5000 - Loss: -0.460  res: 0.00000377\n",
      "Iter 4000/5000 - Loss: -0.460  res: 0.00000055\n",
      "Iter 5000/5000 - Loss: -0.460  res: 0.00000037\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.972094 \n",
      "\n",
      "Multi-objective optimization #RUN 7\n",
      "Iter 1000/5000 - Loss: -1.794  res: 0.00000965\n",
      "Iter 2000/5000 - Loss: -1.807  res: 0.00000295\n",
      "Iter 3000/5000 - Loss: -1.813  res: 0.00000170\n",
      "Iter 4000/5000 - Loss: -1.816  res: 0.00000251\n",
      "Iter 5000/5000 - Loss: -1.818  res: 0.00001211\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 192.561  res: 0.00063861\n",
      "Iter 2000/5000 - Loss: 119.272  res: 0.00038191\n",
      "Iter 3000/5000 - Loss: 85.263  res: 0.00029813\n",
      "Iter 4000/5000 - Loss: 65.142  res: 0.00024263\n",
      "Iter 5000/5000 - Loss: 52.258  res: 0.00020044\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.403  res: 0.00001890\n",
      "Iter 2000/5000 - Loss: -0.407  res: 0.00000591\n",
      "Iter 3000/5000 - Loss: -0.409  res: 0.00000296\n",
      "Iter 4000/5000 - Loss: -0.410  res: 0.00000181\n",
      "Iter 5000/5000 - Loss: -0.410  res: 0.00000122\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998350 \n",
      "\n",
      "Multi-objective optimization #RUN 8\n",
      "Iter 1000/5000 - Loss: -1.829  res: 0.00001304\n",
      "Iter 2000/5000 - Loss: -1.843  res: 0.00000425\n",
      "Iter 3000/5000 - Loss: -1.848  res: 0.00000218\n",
      "Iter 4000/5000 - Loss: -1.852  res: 0.00000134\n",
      "Iter 5000/5000 - Loss: -1.854  res: 0.00000030\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 184.501  res: 0.00063077\n",
      "Iter 2000/5000 - Loss: 115.103  res: 0.00037514\n",
      "Iter 3000/5000 - Loss: 82.705  res: 0.00029647\n",
      "Iter 4000/5000 - Loss: 62.972  res: 0.00025078\n",
      "Iter 5000/5000 - Loss: 49.993  res: 0.00021148\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.411  res: 0.00001798\n",
      "Iter 2000/5000 - Loss: -0.415  res: 0.00000494\n",
      "Iter 3000/5000 - Loss: -0.417  res: 0.00000252\n",
      "Iter 4000/5000 - Loss: -0.418  res: 0.00000151\n",
      "Iter 5000/5000 - Loss: -0.418  res: 0.00000114\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998491 \n",
      "\n",
      "Multi-objective optimization #RUN 9\n",
      "Iter 1000/5000 - Loss: -1.761  res: 0.00000860\n",
      "Iter 2000/5000 - Loss: -1.769  res: 0.00000190\n",
      "Iter 3000/5000 - Loss: -1.772  res: 0.00009859\n",
      "Iter 4000/5000 - Loss: -1.774  res: 0.00000561\n",
      "Iter 5000/5000 - Loss: -1.775  res: 0.00000078\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 187.727  res: 0.00061532\n",
      "Iter 2000/5000 - Loss: 118.471  res: 0.00036689\n",
      "Iter 3000/5000 - Loss: 85.566  res: 0.00029492\n",
      "Iter 4000/5000 - Loss: 64.921  res: 0.00025990\n",
      "Iter 5000/5000 - Loss: 50.805  res: 0.00022997\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.374  res: 0.00001916\n",
      "Iter 2000/5000 - Loss: -0.378  res: 0.00000567\n",
      "Iter 3000/5000 - Loss: -0.379  res: 0.00000278\n",
      "Iter 4000/5000 - Loss: -0.380  res: 0.00000149\n",
      "Iter 5000/5000 - Loss: -0.381  res: 0.00000353\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998491 \n",
      "\n",
      "Multi-objective optimization #RUN 10\n",
      "Iter 1000/5000 - Loss: -1.703  res: 0.00000530\n",
      "Iter 2000/5000 - Loss: -1.708  res: 0.00000247\n",
      "Iter 3000/5000 - Loss: -1.710  res: 0.00000086\n",
      "Iter 4000/5000 - Loss: -1.712  res: 0.00001862\n",
      "Iter 5000/5000 - Loss: -1.712  res: 0.00000035\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 180.623  res: 0.00061066\n",
      "Iter 2000/5000 - Loss: 114.548  res: 0.00036216\n",
      "Iter 3000/5000 - Loss: 83.095  res: 0.00029107\n",
      "Iter 4000/5000 - Loss: 63.222  res: 0.00025849\n",
      "Iter 5000/5000 - Loss: 49.453  res: 0.00023246\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.441  res: 0.00001670\n",
      "Iter 2000/5000 - Loss: -0.445  res: 0.00002042\n",
      "Iter 3000/5000 - Loss: -0.447  res: 0.00000237\n",
      "Iter 4000/5000 - Loss: -0.448  res: 0.00000845\n",
      "Iter 5000/5000 - Loss: -0.448  res: 0.00000094\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1154   RI: 1.828000   Tg: 381.251354   TC:0.553000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998491 \n",
      "\n",
      "Multi-objective optimization #RUN 11\n",
      "Iter 1000/5000 - Loss: -1.761  res: 0.00001097\n",
      "Iter 2000/5000 - Loss: -1.772  res: 0.00000344\n",
      "Iter 3000/5000 - Loss: -1.776  res: 0.00000170\n",
      "Iter 4000/5000 - Loss: -1.778  res: 0.00000009\n",
      "Iter 5000/5000 - Loss: -1.780  res: 0.00000332\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 170.749  res: 0.00061148\n",
      "Iter 2000/5000 - Loss: 108.289  res: 0.00036151\n",
      "Iter 3000/5000 - Loss: 78.638  res: 0.00028978\n",
      "Iter 4000/5000 - Loss: 59.910  res: 0.00025719\n",
      "Iter 5000/5000 - Loss: 46.920  res: 0.00023134\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.436  res: 0.00001246\n",
      "Iter 2000/5000 - Loss: -0.439  res: 0.00000373\n",
      "Iter 3000/5000 - Loss: -0.440  res: 0.00000185\n",
      "Iter 4000/5000 - Loss: -0.441  res: 0.00000113\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter 5000/5000 - Loss: -0.441  res: 0.00000075\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1200   RI: 1.763000   Tg: 395.536308   TC:0.800000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998491 \n",
      "\n",
      "Multi-objective optimization #RUN 12\n",
      "Iter 1000/5000 - Loss: -1.701  res: 0.00000997\n",
      "Iter 2000/5000 - Loss: -1.710  res: 0.00000295\n",
      "Iter 3000/5000 - Loss: -1.714  res: 0.00000147\n",
      "Iter 4000/5000 - Loss: -1.716  res: 0.00000087\n",
      "Iter 5000/5000 - Loss: -1.717  res: 0.00000060\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 166.738  res: 0.00060228\n",
      "Iter 2000/5000 - Loss: 106.490  res: 0.00035558\n",
      "Iter 3000/5000 - Loss: 77.750  res: 0.00028493\n",
      "Iter 4000/5000 - Loss: 59.482  res: 0.00025376\n",
      "Iter 5000/5000 - Loss: 46.694  res: 0.00023018\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.453  res: 0.00001108\n",
      "Iter 2000/5000 - Loss: -0.456  res: 0.00000331\n",
      "Iter 3000/5000 - Loss: -0.457  res: 0.00000164\n",
      "Iter 4000/5000 - Loss: -0.457  res: 0.00000098\n",
      "Iter 5000/5000 - Loss: -0.458  res: 0.00000067\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1200   RI: 1.763000   Tg: 395.536308   TC:0.800000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998630 \n",
      "\n",
      "Multi-objective optimization #RUN 13\n",
      "Iter 1000/5000 - Loss: -1.710  res: 0.00000990\n",
      "Iter 2000/5000 - Loss: -1.719  res: 0.00000282\n",
      "Iter 3000/5000 - Loss: -1.722  res: 0.00000139\n",
      "Iter 4000/5000 - Loss: -1.724  res: 0.00000084\n",
      "Iter 5000/5000 - Loss: -1.725  res: 0.00005853\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 160.612  res: 0.00059892\n",
      "Iter 2000/5000 - Loss: 102.916  res: 0.00035216\n",
      "Iter 3000/5000 - Loss: 75.408  res: 0.00028133\n",
      "Iter 4000/5000 - Loss: 57.880  res: 0.00025101\n",
      "Iter 5000/5000 - Loss: 45.509  res: 0.00023005\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.494  res: 0.00002655\n",
      "Iter 2000/5000 - Loss: -0.501  res: 0.00000798\n",
      "Iter 3000/5000 - Loss: -0.504  res: 0.00000396\n",
      "Iter 4000/5000 - Loss: -0.505  res: 0.00000241\n",
      "Iter 5000/5000 - Loss: -0.506  res: 0.00000162\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1200   RI: 1.763000   Tg: 395.536308   TC:0.800000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998630 \n",
      "\n",
      "Multi-objective optimization #RUN 14\n",
      "Iter 1000/5000 - Loss: -1.713  res: 0.00000760\n",
      "Iter 2000/5000 - Loss: -1.720  res: 0.00000213\n",
      "Iter 3000/5000 - Loss: -1.723  res: 0.00000103\n",
      "Iter 4000/5000 - Loss: -1.724  res: 0.00000061\n",
      "Iter 5000/5000 - Loss: -1.725  res: 0.00000052\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 158.431  res: 0.00059220\n",
      "Iter 2000/5000 - Loss: 102.198  res: 0.00034576\n",
      "Iter 3000/5000 - Loss: 75.339  res: 0.00027544\n",
      "Iter 4000/5000 - Loss: 58.160  res: 0.00024557\n",
      "Iter 5000/5000 - Loss: 45.945  res: 0.00022628\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.514  res: 0.00002452\n",
      "Iter 2000/5000 - Loss: -0.521  res: 0.00000713\n",
      "Iter 3000/5000 - Loss: -0.523  res: 0.00000354\n",
      "Iter 4000/5000 - Loss: -0.525  res: 0.00000217\n",
      "Iter 5000/5000 - Loss: -0.526  res: 0.00000084\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1200   RI: 1.763000   Tg: 395.536308   TC:0.800000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998630 \n",
      "\n",
      "Multi-objective optimization #RUN 15\n",
      "Iter 1000/5000 - Loss: -1.729  res: 0.00000767\n",
      "Iter 2000/5000 - Loss: -1.736  res: 0.00000219\n",
      "Iter 3000/5000 - Loss: -1.738  res: 0.00000106\n",
      "Iter 4000/5000 - Loss: -1.740  res: 0.00000064\n",
      "Iter 5000/5000 - Loss: -1.741  res: 0.00000135\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 152.501  res: 0.00058961\n",
      "Iter 2000/5000 - Loss: 98.624  res: 0.00034309\n",
      "Iter 3000/5000 - Loss: 72.913  res: 0.00027233\n",
      "Iter 4000/5000 - Loss: 56.469  res: 0.00024214\n",
      "Iter 5000/5000 - Loss: 44.777  res: 0.00022217\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.521  res: 0.00002523\n",
      "Iter 2000/5000 - Loss: -0.529  res: 0.00000746\n",
      "Iter 3000/5000 - Loss: -0.531  res: 0.00000356\n",
      "Iter 4000/5000 - Loss: -0.533  res: 0.00000215\n",
      "Iter 5000/5000 - Loss: -0.534  res: 0.00000144\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1200   RI: 1.763000   Tg: 395.536308   TC:0.800000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998630 \n",
      "\n",
      "Multi-objective optimization #RUN 16\n",
      "Iter 1000/5000 - Loss: -1.710  res: 0.00000747\n",
      "Iter 2000/5000 - Loss: -1.716  res: 0.00000218\n",
      "Iter 3000/5000 - Loss: -1.719  res: 0.00000106\n",
      "Iter 4000/5000 - Loss: -1.720  res: 0.00000063\n",
      "Iter 5000/5000 - Loss: -1.721  res: 0.00000877\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: 147.372  res: 0.00058658\n",
      "Iter 2000/5000 - Loss: 95.657  res: 0.00033872\n",
      "Iter 3000/5000 - Loss: 71.106  res: 0.00026574\n",
      "Iter 4000/5000 - Loss: 55.519  res: 0.00023238\n",
      "Iter 5000/5000 - Loss: 44.531  res: 0.00020927\n",
      "### complete ###\n",
      "Iter 1000/5000 - Loss: -0.520  res: 0.00002089\n",
      "Iter 2000/5000 - Loss: -0.528  res: 0.00000681\n",
      "Iter 3000/5000 - Loss: -0.535  res: 0.00001899\n",
      "Iter 4000/5000 - Loss: -0.538  res: 0.00000242\n",
      "Iter 5000/5000 - Loss: -0.539  res: 0.00000306\n",
      "### complete ###\n",
      "Current best candidate\n",
      " polymer_ID: PI1200   RI: 1.763000   Tg: 395.536308   TC:0.800000 \n",
      "\n",
      "\u001b[1mCurrent hyperparameter volume is\u001b[0m 0.998630 \n",
      "\n",
      "###Finished###\n",
      " Total 100 calcultions\n",
      "Run time 931.8 seconds\n"
     ]
    }
   ],
   "source": [
    "YOPTID=[]\n",
    "HyperVolume=[]\n",
    "start = time.perf_counter()\n",
    "log(True)\n",
    "print (\"Randomly selected %d data for initial training \" %(random_data))\n",
    "Xtrain,XtraininfoIni,Ytrain,Xremain,XremaininfoIni,Yremain  =Initialdata_Preparation (X_all,Xinfo,Ydata,random_data,n_layer,nobj,seed)\n",
    "Paretoloc, ybestloc = paretoOpt(Ytrain,metric=metr,opt='max') \n",
    "Calpoly=Ytrain\n",
    "CalpolyID=XtraininfoIni\n",
    "YOPT=Ytrain[Paretoloc]\n",
    "volume=HV.compute(YOPT)\n",
    "HyperVolume.append(volume/volumemax)\n",
    "ybest=Ytrain[ybestloc]\n",
    "ybestid=XtraininfoIni[ybestloc]\n",
    "YBESTID=ybestid\n",
    "YBEST=ybest\n",
    "Calpoly=Ytrain\n",
    "CalpolyID=XtraininfoIni\n",
    "print (\"The best from random datasets\\n polymer_ID: %s   RI: %f   Tg: %f   TC:%f \\n\" %(ybestid,ybest[0],ybest[1],ybest[2]))\n",
    "print (\"\\033[1mCurrent hyperparameter volume is\\033[0m %f \\n\" %(HyperVolume[-1]))\n",
    "for iii in range(Nruns):\n",
    "    print (\"Multi-objective optimization #RUN %d\" %(iii+1))\n",
    "    Xtrainnew,XtraininfoIninew,Ytrainnew,Xremainnew,XremaininfoIninew,Yremainnew,Xexplored,Xexploredinfo,Yexplored,ybestnew,ybestidnew,result=\\\n",
    "MBO_opt(Xtrain,XtraininfoIni,Ytrain,Xremain,XremaininfoIni,Yremain,nobj,n_layer,Multiprobe,X_layer,ybest,metr)\n",
    "    print (\"Current best candidate\\n polymer_ID: %s   RI: %f   Tg: %f   TC:%f \\n\" %(ybestidnew,ybestnew[0],ybestnew[1],ybestnew[2]))\n",
    "    print (\"\\033[1mCurrent hyperparameter volume is\\033[0m %f \\n\" %(HyperVolume[-1]))\n",
    "    YOPT=np.append(YOPT,Yexplored).reshape(-1, nobj)\n",
    "    YBEST=np.append(YBEST,ybestnew).reshape(-1, nobj)\n",
    "    YBESTID=np.append(YBESTID,ybestidnew).reshape(-1,1)\n",
    "    YOPTID=np.append(YOPTID,Xexploredinfo)\n",
    "    Calpoly=np.append(Calpoly,Yexplored).reshape(-1, nobj)\n",
    "    CalpolyID=np.append(CalpolyID,Xexploredinfo)\n",
    "    Paretoloc, ybestloc = paretoOpt(Calpoly,metric=metr,opt='max') \n",
    "    volume=HV.compute(YOPT)\n",
    "    HyperVolume.append(volume/volumemax)\n",
    "    Xtrain=Xtrainnew\n",
    "    XtraininfoIni=XtraininfoIninew\n",
    "    Ytrain=Ytrainnew\n",
    "    Xremain=Xremainnew\n",
    "    XremaininfoIni=XremaininfoIninew\n",
    "    Yremain=Yremainnew\n",
    "    ybest=ybestnew\n",
    "print (\"###Finished###\\n Total %d calcultions\" %(random_data+Nruns*Multiprobe))\n",
    "end = time.perf_counter()\n",
    "print(\"Run time\", round(end-start,2), 'seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f4bbe089",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.isfile(\"../runtime.txt\") is True:\n",
    "    os.remove(\"../runtime.txt\")\n",
    "log0 = open(\"../runtime.txt\",mode=\"a\",encoding=\"utf-8\")\n",
    "print(\"tutorial_MBO.log\",file = log0)\n",
    "print(\"Initial Structures\", random_data,file = log0)\n",
    "print(\"Multiprobe\", Multiprobe,file = log0)\n",
    "print(\"Runs\", Nruns,file = log0)\n",
    "print(\"Run time\", round(end-start,2), 'seconds',file = log0)\n",
    "log0.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d731c8e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_=np.array(df[df.columns[0:2]])\n",
    "CALSMILES=[df_[i][1] for i in range (len(df_)) if df_[i][0] in CalpolyID]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "36197af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = [x for x in CalpolyID]\n",
    "b = [x for x in CALSMILES]\n",
    "c = [x for x in Calpoly[:,0]]\n",
    "d = [x for x in Calpoly[:,1]]\n",
    "e = [x for x in Calpoly[:,2]]\n",
    "g=[random_data+num*Multiprobe for num in range(Nruns+1)]\n",
    "h = [x for x in HyperVolume]\n",
    "cal_df = pd.DataFrame({'Cal_ID':a,'SMILES':b,'RI':c,'Tg':d,'TC':e})\n",
    "HV_df = pd.DataFrame({'Count':g,'HyperVolume':h})\n",
    "cal_df.to_csv(r'../cal_data.csv')\n",
    "HV_df.to_csv(r'../HV.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d235c4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "746205f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,3.5))\n",
    "plt.rc('font',family='Arial',weight='normal')\n",
    "font1 = {'family' : 'Arial',\n",
    "'weight' : 'normal',\n",
    "'size'   : 16,\n",
    "}\n",
    "plt.plot(g,h,color='r',linestyle='-',linewidth=1.5,zorder=2)\n",
    "plt.hlines(0.98,0, 100, \"gray\",linestyles='--',linewidth=1.5,zorder=0) \n",
    "title='Number of candidates'\n",
    "title1='Hypervolume (%)'\n",
    "plt.xlabel(title,font1 )\n",
    "plt.ylabel(title1,font1 )\n",
    "plt.xlim((20, 100))\n",
    "plt.ylim((0.6, 1.02))\n",
    "my_x_ticks = np.arange(20,101,20)\n",
    "my_y_ticks = np.arange(0.6, 1.01,0.1)\n",
    "plt.xticks(my_x_ticks,size=14)\n",
    "plt.yticks(my_y_ticks,size=14)\n",
    "plt.tick_params(width=1.5)\n",
    "bwith = 1.5\n",
    "TK = plt.gca()\n",
    "TK.spines['bottom'].set_linewidth(bwith)\n",
    "TK.spines['left'].set_linewidth(bwith)\n",
    "TK.spines['top'].set_linewidth(bwith)\n",
    "TK.spines['right'].set_linewidth(bwith)\n",
    "plt.savefig(r'../HV_ParMEI.png', dpi=300,transparent=True,bbox_inches = 'tight')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}